论文标题
隐私保护测试的优化算法，用于流行病。
A privacy-preserving tests optimization algorithm for epidemics containment
论文作者
论文摘要
SARS-COV-2爆发改变了世界上几乎所有人的日常生活。目前，我们面临的问题是，使用更有效的强迫锁定，这既有病毒的传播，这具有减缓所涉及国家经济的缩短，并且通过确定和隔离积极的个体，而这是一般缺乏信息，这是一项艰巨的任务。对于这种特异性疾病，由于存在天主教徒，即积极且潜在传染性的，但没有表现出任何SARS-COV-2的症状，因此受感染的识别尤其具有挑战性。直到疫苗的开发和分配尚未准备就绪之前，我们需要设计选择那些最有可能感染的个人的方法，鉴于每天可用的测试量有限。在本文中，我们利用由所谓的接触跟踪应用程序收集的可用数据来开发一种算法，即PPTO，这些算法识别那些很可能是正面的人，因此应测试。虽然过去这些分析是通过集中式算法进行的，但要求所有应用程序用户数据都收集在一个数据库中，我们的协议能够通过利用匿名信息到其他设备的通信来在设备级别上工作。
The SARS-CoV-2 outbreak changed the everyday life of almost all the people over the world.Currently, we are facing with the problem of containing the spread of the virus both using the more effective forced lockdown, which has the drawback of slowing down the economy of the involved countries, and by identifying and isolating the positive individuals, which, instead, is an hard task in general due to the lack of information. For this specific disease, the identificato of the infected is particularly challenging since there exists cathegories, namely the asymptomatics, who are positive and potentially contagious, but do not show any of the symptoms of SARS-CoV-2. Until the developement and distribution of a vaccine is not yet ready, we need to design ways of selecting those individuals which are most likely infected, given the limited amount of tests which are available each day. In this paper, we make use of available data collected by the so called contact tracing apps to develop an algorithm, namely PPTO, that identifies those individuals that are most likely positive and, therefore, should be tested. While in the past these analysis have been conducted by centralized algorithms, requiring that all the app users data are gathered in a single database, our protocol is able to work on a device level, by exploiting the communication of anonymized information to other devices.
